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on Transition Economics |
| By: | Whelsy BOUNGOU; Alhonita YATIE |
| Abstract: | As a topical topic, this paper studies the responses of world stock market indices to the ongoing war between Ukraine and Russia. The empirical analysis is based on daily stock market returns in a sample of 94 countries and covers the period from 22 January 2022 to 3 March 2022. We consistently document a negative relationship between the Ukraine-Russia war and world stock market returns. Furthermore, our results reveal that returns have been significantly lower since the invasion of Russian troops into Ukraine on 24 February 2022. Overall, we provide the first empirical evidence of the effect of the Ukraine-Russia war on international stock market returns.As a topical topic, this paper studies the responses of world stock market indices to the ongoing war between Ukraine and Russia. The empirical analysis is based on daily stock market returns in a sample of 94 countries and covers the period from 22 January 2022 to 3 March 2022. We consistently document a negative relationship between the Ukraine-Russia war and world stock market returns. Furthermore, our results reveal that returns have been significantly lower since the invasion of Russian troops into Ukraine on 24 February 2022. Overall, we provide the first empirical evidence of the effect of the Ukraine-Russia war on international stock market returns. |
| Keywords: | War, Russia, Ukraine, Stock index |
| JEL: | H56 G11 G14 G15 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:grt:bdxewp:2022-06&r= |
| By: | Adachi, Yuko; Iwasaki, Ichiro |
| Abstract: | This paper traces the survival status of 93,260 Russian business firms in the period of 2007–2019 and empirically examines the determinants of the acquisition of financially distressed companies (i.e., distressed acquisitions). We found that, of 93,260 firms, 50,743 failed in management, and among these distressed firms, 10,110 were rescued by acquisition during the observation period. Our empirical results indicate that, in Russian regions, the weakness of the legal system tends to increase the probability of distressed acquisitions, while other socioeconomic risks negatively affect it. These tendencies are common in most industries and regions. It is also revealed that, in the most developed area, monotown enterprises are more likely to be bailed out by acquisition after management failure than other firms, but it is not always true for the whole nation. |
| Keywords: | legal weakness, investment risk, financial distress, distressed acquisitions, Russia |
| JEL: | C35 D02 D22 E02 G34 K20 L22 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:hit:rrcwps:98&r= |
| By: | Ana Lugo, Maria; Raiser, Martin; Yemtsov, Ruslan |
| Abstract: | The present document examines the role of social and economic transformation in the process of poverty reduction in China. China's economic growth and poverty reduction over the past 40 years are historically unprecedented both in speed and scale. Between 1978 and 2018 China's economy grew at an annual rate of 9.5 percent, twice as fast as the other developing regions of the World. The proportion of those living in poverty in rural areas fell from 97.5 percent to less than one percent over this period. There are 765 million fewer poor people in China in 2019 than they were in 1980. This means that China alone accounts for three quarters of the total reduction in global extreme poverty in that period. Although China's growth rate will continue to slow in the coming decades, the scope for economic convergence through structural transformation has not yet been fully exhausted. Further urbanization and associated productivity increases can continue to play a critical role for poverty reduction in China. However, for progress to be sustained a number of adjustments to policy will be needed. |
| Keywords: | poverty reduction,economic transformation,rural-urban migration,China |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:kcgpps:8&r= |
| By: | Patrik Barišić (Croatian National Bank); Tibor Kovač (Institute of Economics, Zagreb); Vladimir Arčabić (Faculty of Economics and Business, University of Zagreb) |
| Abstract: | This paper separates macroeconomic shocks into external and domestic aggregate demand and supply shocks in European Union's post-transition countries. Small open economies are typically very responsive to external shocks. The standard decomposition into aggregate demand and supply shocks covers up important information on the sources of business cycle fluctuations. Using a Bayesian SVAR model with combined sign and block exogeneity restrictions, we separately estimate external and domestic aggregate supply and demand shocks for GDP growth and inflation. We find that domestic shocks were a dominant source of fluctuations during the transition period in Croatia from 1992 to 2000. However, external shocks increased their importance with the trade and financial sector liberalization after 2000, becoming the dominant source of fluctuations with the Global financial crisis in 2008. In the short run, fluctuations are best explained by domestic shocks in 9 out of 11 analyzed countries, especially domestic supply shocks. However, in the medium run, fluctuations are dominantly explained by external aggregate demand shocks in 8 out of 11 countries. We argue that common sources of fluctuations in the medium run are beneficial for common monetary policy in the Eurozone. |
| Keywords: | small open economy, post-transition countries, aggregate supply and demand shocks, external and domestic shocks, Bayesian SVAR |
| JEL: | C32 C51 E32 F41 |
| Date: | 2022–03–28 |
| URL: | http://d.repec.org/n?u=RePEc:zag:wpaper:2202&r= |
| By: | Jurić, Tado |
| Abstract: | Background: This paper shows that Big Data and the so-called tools of digital demography, such as Google Trends (GT) and insights from social networks such as Instagram, Twitter and Facebook, can be useful for determining, estimating, and predicting the forced migration flows to the EU caused by the war in Ukraine. Objective: The objective of this study was to test the usefulness of Google Trends indexes to predict further forced migration from Ukraine to the EU (mainly to Germany) and gain demographic insights from social networks into the age and gender structure of refugees. Methods: The primary methodological concept of our approach is to monitor the digital trace of Internet searches in Ukrainian, Russian and English with the Google Trends analytical tool (trends.google.com). Initially, keywords were chosen that are most predictive, specific, and common enough to predict the forced migration from Ukraine. We requested the data before and during the war outbreak and divided the keyword frequency for each migration-related query to standardise the data. We compared this search frequency index with official statistics from UNHCR to prove the significations of results and correlations and test the models predictive potential. Since UNHCR does not yet have complete data on the demographic structure of refugees, to fill this gap, we used three other alternative Big Data sources: Facebook, Twitter and Instagram. Results: All tested migration-related search queries about emigration planning from Ukraine show the positive linear association between Google index and data from official UNHCR statistics; R2 = 0.1211 for searches in Russian and R2 = 0.1831 for searches in Ukrainian. It is noticed that Ukrainians use the Russian language more often to search for terms than Ukrainian. Increase in migration-related search activities in Ukraine such as граница (Rus. border), кордону (Ukr. border); Польща (Poland); Германия (Rus. Germany), Німеччина (Ukr. Germany) and Угорщина and Венгрия (Hungary) correlate strongly with officially UNHCR data for externally displaced persons from Ukraine. All three languages show that the interest in Poland is the highest. When refugees arrive in nearby countries, the search for terms related to Germany, such as crossing the border + Germany, etc., is proliferating. This result confirms our hypothesis that one-third of all refugees will cross into Germany. According to Big Data insights, the estimate of the total number of expected refugees is to expect 5,4 Million refugees. The age group most represented is between 24 and 45 years (data for children are unavailable), and over 65% are women. Conclusion: The increase in migration-related search queries is correlated with the rise in the number of refugees from Ukraine in the EU. Thus this method allows reliable forecasts. Understanding the consequences of forced migration from Ukraine is crucial to enabling UNHCR and governments to develop optimal humanitarian strategies and prepare for refugee reception and possible integration. The benefit of this method is reliable estimates and forecasting that can allow governments and UNHCR to prepare and better respond to the recent humanitarian crisis. |
| Keywords: | refugee,Ukraine,Big Data,forced migration,Google Trends,UNHCR |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:251215&r= |
| By: | Köppl-Turyna, Monika; Kantorowicz, Jarosław |
| Abstract: | This work looks at the impact of electoral rules on female participation in local legislative bodies using a natural experiment involving a series of changes to electoral law in Poland. Using an exogenous population threshold dividing municipalities into ones with proportional and ones with majoritarian elections, we estimate the effect of each electoral system on female representation. Contrary to the literature on the national elections, we ftnd that more females are elected to local councils under a majoritarian system. We link this observation to countering party bias in list placements and lower costs of electoral participation in the majoritarian system. |
| Keywords: | electoral rules,forms of government,female representation,regression discontinuity |
| JEL: | D72 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ecoarp:18&r= |
| By: | Martin Hodula; Milan Szabo; Lukas Pfeifer; Martin Melecky |
| Abstract: | This paper studies the effects of regulatory recommendations concerning maximum (i) loan-to-value (LTV), (ii) debt-to-income (DTI) and (iii) debt service-to-income ratios (DSTI) on new loans secured by residential property. It uses loan-level regulatory survey data on about 82,000 newly granted residential mortgage loans in the Czech Republic from 2016 to 2019 to estimate the average effects of the Czech National Bank's regulatory recommendations and their heterogeneous effects depending on borrower, loan, bank and regional characteristics. The studied response variables include the mortgage loan size and lending rate and the value of the property with which loans are secured. The machine learning method of causal forests is employed to estimate the effects of interest and to identify any heterogeneity and its likely drivers. We highlight two important facts: (i) value-based (LTV) and income-based (DTI and DSTI) limits have different impacts on the mortgage market and (ii) borrower, loan, bank and regional characteristics play an important role in the transmission of the recommended limits. |
| Keywords: | Borrower-based measures, causal forests, Czech Republic, macroprudential recommendations, residential mortgage loans |
| JEL: | E44 G21 G28 G51 R31 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:cnb:wpaper:2022/3&r= |
| By: | , Le Thanh Tung |
| Abstract: | Vietnam is an Asian emerging country, which now is ranked in the group of the fastest-gro-wing economies worldwide. However, this economy has faced galloping inflation in recent years. So the Vietnamese experience is a valuable reference for the policymakers in the developing world in order to successfully control price volatility. Our study applies the Vector autoregressive method, the Johansen cointegration test, and the Granger causality test to examine the impact of fiscal and monetary policy on price volatility in Vietnam with a quarterly data sample collected over the period from 2004 to 2018. The study results confirm the existence of a long-term cointegration relationship between these policies and price volatility in Vietnam. Besides, the variance decomposition and impulse response function also show that the impact of these policies on inflation is clear, however, the fiscal policy more strongly affects inflation than the monetary policy. Finally, the Granger causality test also indicates one-way causality relationships from the government expenditure as well as the exchange rate to price volatility in the study period. |
| Date: | 2021–06–05 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:7u56v&r= |